Papers by Xiangrui Cai
Incorporating Circumstances into Narrative Event Prediction (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies focus on mining the inter-events relationships while ignoring how the events happened. |
| Approach: | They propose to incorporate event circumstances into the narrative event prediction by combining two multi-head attention modules and regularizing attention weights. |
| Outcome: | The proposed model outperforms baseline models by 12.2%. |
From Alignment to Entailment: A Unified Textual Entailment Framework for Entity Alignment (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods encode the triples of entities as embeddings and learn to align the embeddables, which prevents the direct interaction between the original information of the cross-KG entities. |
| Approach: | They propose to transform the triples into unified textual sequences and model the EA task as a bi-directional textual entailment task between the sequences of cross-KG entities. |
| Outcome: | The proposed approach outperforms the state-of-the-art methods on five cross-lingual datasets and allows the mutual enhancement of the heterogeneous information. |
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods to predict missing entities share relation representation across modalities, which results in mutual interference between modality. |
| Approach: | They propose a framework for multimodal knowledge graph completion that learns modality-split relation embeddings for each modality instead of a single modality shared one. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three KG datasets. |
TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for automating taxonomy completion use subtasks to learn subtask results, ignoring the effects of subtask on the final prediction. |
| Approach: | They propose a multi-task automatic taxonomy completion method that attaches emerging concepts to an appropriate pair of hypernym and hyponym in existing taxonomies. |
| Outcome: | The proposed method improves on three datasets and improves inference efficiency. |
BioFEG: Generate Latent Features for Biomedical Entity Linking (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to biomedical entity linking suffer from multiple types of errors due to the rarity of many biomedically relevant entities in real-world scenarios. |
| Approach: | They propose a latent feature generation framework to generate latent semantic features for unseen entities to capture fine-grained coherence information of unseened entities. |
| Outcome: | The proposed framework is superior to existing models on two benchmark datasets. |
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization (2021.acl-long)
Copied to clipboard
| Challenge: | Existing models for medical named entity recognition and named entity normalization suffer from error propagation between the two tasks. |
| Approach: | They propose an end-to-end progressive multi-task learning model for jointly modeling medical named entity recognition and normalization in an effective way. |
| Outcome: | The proposed model reduces error propagation by exploiting the learnable features for both tasks to improve performance. |